A risk-neutral approach to decision making involves learning strategies (sequence of actions) to maximize an expected reward, where the reward signal is provided by the environment.
Learning strategies to satisfy risk-neutral objectives have been successfully implemented in multiple domains, including robotics, autonomous vehicles, games, and mobile networks [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}.
Although these methods to learn strategies are tractable and efficient, rational and risk-neutral decision making using the expected utility is often not sufficient to model decision making in real-world CPS.
